![mockingbird](https://user-images.githubusercontent.com/12797292/131216767-6eb251d6-14fc-4951-8324-2722f0cd4c63.jpg) [![MIT License](https://img.shields.io/badge/license-MIT-blue.svg?style=flat)](http://choosealicense.com/licenses/mit/) > English | [中文](README-CN.md) ## Features 🌍 **Chinese** supported mandarin and tested with multiple datasets: aidatatang_200zh, magicdata, aishell3, and etc. 🤩 **PyTorch** worked for pytorch, tested in version of 1.9.0(latest in August 2021), with GPU Tesla T4 and GTX 2060 🌍 **Windows + Linux** run in both Windows OS and linux OS (even in M1 MACOS) 🤩 **Easy & Awesome** effect with only newly-trained synthesizer, by reusing the pretrained encoder/vocoder 🌍 **Webserver Ready** to serve your result with remote calling ### [DEMO VIDEO](https://www.bilibili.com/video/BV1sA411P7wM/) ## Quick Start ### 1. Install Requirements > Follow the original repo to test if you got all environment ready. **Python 3.7 or higher ** is needed to run the toolbox. * Install [PyTorch](https://pytorch.org/get-started/locally/). > If you get an `ERROR: Could not find a version that satisfies the requirement torch==1.9.0+cu102 (from versions: 0.1.2, 0.1.2.post1, 0.1.2.post2 )` This error is probably due to a low version of python, try using 3.9 and it will install successfully * Install [ffmpeg](https://ffmpeg.org/download.html#get-packages). * Run `pip install -r requirements.txt` to install the remaining necessary packages. * Install webrtcvad `pip install webrtcvad-wheels`(If you need) > Note that we are using the pretrained encoder/vocoder but synthesizer, since the original model is incompatible with the Chinese sympols. It means the demo_cli is not working at this moment. ### 2. Prepare your models You can either train your models or use existing ones: #### 2.1. Train synthesizer with your dataset * Download dataset and unzip: make sure you can access all .wav in folder * Preprocess with the audios and the mel spectrograms: `python pre.py ` Allowing parameter `--dataset {dataset}` to support aidatatang_200zh, magicdata, aishell3, etc. * Train the synthesizer: `python synthesizer_train.py mandarin /SV2TTS/synthesizer` * Go to next step when you see attention line show and loss meet your need in training folder *synthesizer/saved_models/*. #### 2.2 Use pretrained model of synthesizer > Thanks to the community, some models will be shared: | author | Download link | Preview Video | Info | | --- | ----------- | ----- |----- | |@FawenYo | https://drive.google.com/file/d/1H-YGOUHpmqKxJ9FRc6vAjPuqQki24UbC/view?usp=sharing [Baidu Pan](https://pan.baidu.com/s/1vSYXO4wsLyjnF3Unl-Xoxg) Code：1024 | [input](https://github.com/babysor/MockingBird/wiki/audio/self_test.mp3) [output](https://github.com/babysor/MockingBird/wiki/audio/export.wav) | 200k steps with local accent of Taiwan |@miven| https://pan.baidu.com/s/1PI-hM3sn5wbeChRryX-RCQ code：2021 | https://www.bilibili.com/video/BV1uh411B7AD/ #### 2.3 Train vocoder (Optional) > note: vocoder has little difference in effect, so you may not need to train a new one. * Preprocess the data: `python vocoder_preprocess.py ` * Train the wavernn vocoder: `python vocoder_train.py mandarin ` * Train the hifigan vocoder `python vocoder_train.py mandarin hifigan` ### 3. Launch #### 3.1 Using the web server You can then try to run:`python web.py` and open it in browser, default as `http://localhost:8080` #### 3.2 Using the Toolbox You can then try the toolbox: `python demo_toolbox.py -d ` ## Reference > This repository is forked from [Real-Time-Voice-Cloning](https://github.com/CorentinJ/Real-Time-Voice-Cloning) which only support English. | URL | Designation | Title | Implementation source | | --- | ----------- | ----- | --------------------- | | [2010.05646](https://arxiv.org/abs/2010.05646) | HiFi-GAN (vocoder)| Generative Adversarial Networks for Efficient and High Fidelity Speech Synthesis | This repo | |[**1806.04558**](https://arxiv.org/pdf/1806.04558.pdf) | **SV2TTS** | **Transfer Learning from Speaker Verification to Multispeaker Text-To-Speech Synthesis** | This repo | |[1802.08435](https://arxiv.org/pdf/1802.08435.pdf) | WaveRNN (vocoder) | Efficient Neural Audio Synthesis | [fatchord/WaveRNN](https://github.com/fatchord/WaveRNN) | |[1703.10135](https://arxiv.org/pdf/1703.10135.pdf) | Tacotron (synthesizer) | Tacotron: Towards End-to-End Speech Synthesis | [fatchord/WaveRNN](https://github.com/fatchord/WaveRNN) |[1710.10467](https://arxiv.org/pdf/1710.10467.pdf) | GE2E (encoder)| Generalized End-To-End Loss for Speaker Verification | This repo | ## F Q&A #### 1.Where can I download the dataset? [aidatatang_200zh](http://www.openslr.org/62/)、[magicdata](http://www.openslr.org/68/)、[aishell3](http://www.openslr.org/93/) > After unzip aidatatang_200zh, you need to unzip all the files under `aidatatang_200zh\corpus\train` #### 2.What is``? If the dataset path is `D:\data\aidatatang_200zh`,then `` is`D:\data` #### 3.Not enough VRAM Train the synthesizer：adjust the batch_size in `synthesizer/hparams.py` ``` //Before tts_schedule = [(2, 1e-3, 20_000, 12), # Progressive training schedule (2, 5e-4, 40_000, 12), # (r, lr, step, batch_size) (2, 2e-4, 80_000, 12), # (2, 1e-4, 160_000, 12), # r = reduction factor (# of mel frames (2, 3e-5, 320_000, 12), # synthesized for each decoder iteration) (2, 1e-5, 640_000, 12)], # lr = learning rate //After tts_schedule = [(2, 1e-3, 20_000, 8), # Progressive training schedule (2, 5e-4, 40_000, 8), # (r, lr, step, batch_size) (2, 2e-4, 80_000, 8), # (2, 1e-4, 160_000, 8), # r = reduction factor (# of mel frames (2, 3e-5, 320_000, 8), # synthesized for each decoder iteration) (2, 1e-5, 640_000, 8)], # lr = learning rate ``` Train Vocoder-Preprocess the data：adjust the batch_size in `synthesizer/hparams.py` ``` //Before ### Data Preprocessing max_mel_frames = 900, rescale = True, rescaling_max = 0.9, synthesis_batch_size = 16, # For vocoder preprocessing and inference. //After ### Data Preprocessing max_mel_frames = 900, rescale = True, rescaling_max = 0.9, synthesis_batch_size = 8, # For vocoder preprocessing and inference. ``` Train Vocoder-Train the vocoder：adjust the batch_size in `vocoder/wavernn/hparams.py` ``` //Before # Training voc_batch_size = 100 voc_lr = 1e-4 voc_gen_at_checkpoint = 5 voc_pad = 2 //After # Training voc_batch_size = 6 voc_lr = 1e-4 voc_gen_at_checkpoint = 5 voc_pad =2 ``` #### 4.If it happens `RuntimeError: Error(s) in loading state_dict for Tacotron: size mismatch for encoder.embedding.weight: copying a param with shape torch.Size([70, 512]) from checkpoint, the shape in current model is torch.Size([75, 512]).` Please refer to issue [#37](https://github.com/babysor/MockingBird/issues/37) #### 5. How to improve CPU and GPU occupancy rate? Adjust the batch_size as appropriate to improve #### 6. What if it happens `the page file is too small to complete the operation` Please refer to this [video](https://www.youtube.com/watch?v=Oh6dga-Oy10&ab_channel=CodeProf) and change the virtual memory to 100G (102400), for example : When the file is placed in the D disk, the virtual memory of the D disk is changed. #### 7. When should I stop during training? FYI, my attention came after 18k steps and loss became lower than 0.4 after 50k steps. ![attention_step_20500_sample_1](https://user-images.githubusercontent.com/7423248/128587252-f669f05a-f411-4811-8784-222156ea5e9d.png) ![step-135500-mel-spectrogram_sample_1](https://user-images.githubusercontent.com/7423248/128587255-4945faa0-5517-46ea-b173-928eff999330.png)